Three-objective Optimization Research Articles

Performance prediction and manipulation strategy of a hybrid system based on tubular solid oxide fuel cell and annular thermoelectric generator

Abstract Tubular solid oxide fuel cells (TSOFCs) generate high-grade waste heat during operation, but the existing waste heat recovery technologies designed for flat solid oxide fuel cells cannot be directly applied to TSOFC due to the geometry mismatch. To efficient harvest the waste heat, a new geometry-matching hybrid system including TSOFC and annular thermoelectric generator (ATEG) is synergistically integrated to evaluate the performance upper limit. A mathematical model is formulated and verified to describe the hybrid system by considering various thermodynamic-electrochemical irreversible effects. Key performance indicators are established to assess the potential performance. Calculations show that the peak power density and corresponding efficiency of the proposed system are enhanced by 20.39 % and 13.89 %, respectively, compared to a standalone TSOFC. Furthermore, the exergy destruction rate is reduced by 7.04 %. Extensive sensitivity analyses indicate that higher operating temperatures enhance the system’s performance, while larger electrode tortuosity negatively affects it. Additionally, various optimization paths of ATEG are explored to improve the system performance, including considerations such as the number of thermocouples, leg radial width, leg thickness, or annular shape parameter. The three-objective optimization yields an efficient design solution for the entire system, offering valuable insights for its design and operation.

Energy, exergy and exergoeconomic analyses and ANN-based three-objective optimization of a supercritical CO2 recompression Brayton cycle driven by a high-temperature geothermal reservoir

The use of CO2-based power cycles to harness high-temperature geothermal resources is an interesting application that has been little explored. Therefore, to obtain a more holistic understanding of these systems, in this work, a supercritical CO2 recompression Brayton cycle is assessed. For comparison, the performances of a recuperative cycle and a recompression cycle with intercooling were also computed. Detailed mathematical models were developed and then, artificial neural network-based surrogate models were adopted to conduct multi-objective optimization. Results of the baseline simulations show that, the high temperature recuperator and the turbine are the most important components from both exergy and exergoeconomic approaches. The optimizations reveal that the intercooled cycle is superior to the other layouts in all the cases while the recuperative cycle is not feasible with reinjection temperatures greater or equal to 150 °C. When the reinjection temperature is constrained to 150 °C, the intercooled cycle attains 4472.70 kW, 62.10 % and 14.42 $/GJ for net power, exergy efficiency and product unit cost, respectively, whereas without this constraint, it achieves 5394.09 kW, 61.83 %, 11.79 $/GJ. In conclusion, the adoption of advanced layouts of the CO2 cycle is beneficial for this application from both technical and economic points of view.

Economic and technical optimization of a tri-generation setup integrating a partial evaporation Rankine cycle with an ejector-based refrigeration system using different solar collectors

Three collector types, namely parabolic trough solar collector (PTSC), linear Fresnel collector (LFC), and dish collector, are employed to drive a tri-generation configuration composed of a partial evaporation Rankine cycle (PERC), a hot water production unit (HWPU), and a cascade ejector-based compression refrigeration system (ECRS) for electricity, cooling, and heating production. The ECRS is integrated with the topping PERC through an absorption refrigeration system (ARS). Moreover, the collector is indirectly connected to the system by a storage tank to increase the operating hours of the system. The performance of the system is evaluated by thermodynamic and economic analysis. The three-objective optimization result is a testament to the fact that utilizing PTSC brings about the best performance of the system. The system exergy efficiency (ηex) in the case of PTSC is 8.1 % and 24.9 % higher than in the case of LFC and dish collector, respectively. The economic performance of the system in the case of PTSC is superior to that of the dish collector. Furthermore, the PTSC case results in a relatively similar economic performance to the LFC case. The system produces power, cooling, and heating at rates of 200.8 kW, 564.9 kW, and 795.7 kW in the optimum case of utilizing PTSC, respectively. Moreover, ηex, total cost rate (C˙tot), and specific cost of tri-generation (ctri) are obtained as 11.48 %, 204.7 $h−1, and 44.91 $GJ−1, correspondingly. The results indicate that ηex of the proposed layout in the case of PTSC is superior to a comparable tri-generation configuration introduced in the field by 2.78 % points.

Multi-criteria optimization next to a comparative analysis of a polygeneration layout based on a double-stage gas turbine cycle fueled by biomass and hydrogen

The significance and advantages of utilizing renewable energy sources are widely recognized. Upon examining past studies, it was revealed that there has been a lack of research on harnessing the waste energy from double-stage gas turbines powered by biomass-hydrogen hybrid fuel for polygeneration applications. Thus, a new configuration is proposed in the study for the multi-production of electricity, cooling, heating, and potable water. In the current research, a high-pressure turbine is driven by a combustion chamber fed by a biomass gasifier. Meanwhile, a low-pressure turbine is driven by a post-combustion chamber fed by hydrogen as fuel. The heat loss of the topping gas turbine cycle is recuperated by a supercritical Brayton cycle (SBC), a steam Rankine cycle (SRC), and a water heater. The waste heat of the supercritical Brayton cycle and steam Rankine cycle is recovered through an absorption refrigeration cycle and a thermoelectric generator, respectively. Thus, the heat loss of the topping system is completely recovered. The net power output of the gas turbine cycle is considered the electricity production of the system while the output power of the SBC and SRC is utilized in a reverse osmosis desalination system to produce potable water. The investigations conducted on the system are thermodynamic and exergy-economic assessments and three-objective optimization utilizing 5 gases (i.e., CO2, CO, Nitrogen, Air, and Helium) in the SBC. The final results reveal the superiority of Helium as the supercritical gas, bringing about an exergy efficiency () of 32.11%, a total cost rate () of 103 , a payback period (PP) of 0.159 years, and a specific cost of polygeneration () of 22.03 for the system. Although the levelized cost of products of the configuration is high due to the freshwater production, of the proposed system is higher than the previous systems based on a double-stage gas turbine cycle driven by biomass and hydrogen.

Optimized multi-criteria performance of a poly-generation layout including a Stirling engine and a supercritical Brayton cycle using biogas and methane as two potential fuels of a topping gas turbine cycle

The combination of a supercritical Brayton cycle, an absorption chiller, a Stirling engine, a reverse osmosis desalination system, and a proton exchange membrane electrolyzer is investigated for waste heat recovery of a topping gas turbine cycle. The required electricity for hydrogen and freshwater production is supplied by the Stirling engine and supercritical Brayton cycle, respectively. In addition, the output power is provided by the gas turbine cycle, and the exiting cold stream of the Stirling engine is considered hot water for domestic utilization. A comparison is made between biogas and pure methane as two possible fuels for the system. Energy and exergy analyses, economic analysis using the specific exergy costing approach, and environmental analysis considering the carbon dioxide emissions are employed in the study. The three-objective optimization of the configuration discloses an exergy efficiency (ηex) of 39.49 % and 39.85 % in the cases of biogas and methane, respectively. The proposed system can improve ηex by 6.18 % points compared to the stand-alone GTC. The specific cost of poly-generation (cpoly) and the total cost rate (C˙tot) are obtained as 25.92 $GJ−1 and 249.5 $h−1 in the biogas mode, while these values are calculated as 36.75 $GJ−1 and 336.7 $h−1 in the methane case. The environmental cost rate (C˙env) of the system is 24.78 $h−1 in the case of biogas and 17.47 $h−1 in the methane mode. The results confirm the superiority of the methane case from the environmental viewpoint over biogas. However, the low values of cpoly and C˙tot in the biogas case indicate that biogas is superior to methane from a general standpoint. The utilization of the present setup for waste heat recovery of biogas-driven GTCs is recommended due to the higher ηex than the previous layouts.

Emergoenvironmental analysis and sustainable flexible peaking of a novel solar-assisted solid oxide fuel cell cogeneration system

The development of green and sustainable conversion technologies is imperative to alleviate the current severe energy and environmental crises and to achieve the goal of carbon neutrality. This project presents a novel cogeneration system that integrates a solid oxide fuel cell afterburning system, an energy recovery system, and a parallel heating system equipped with multistage utilization, solar-assisted, and flexible peaking technologies. Following the establishment and validation of the system, power source analysis, parameter sensitivity studies, three-objective optimization, and internal exergy flow investigation are carried out. Subsequently, the positive impacts of multistage utilization, solar-assisted, and flexible peaking technologies, as well as the thermoeconomic and emergoenvironmental operating characteristics of the system, are discussed. The numerical results demonstrate that the emergoenvironmental factor and emergy environmental impact difference of the system are 70.15 % and 82.52 %, and the energy efficiency is improved by 1.41 %∼19.09 %, respectively, compared with similar solid oxide fuel cell systems, confirming its favorable thermodynamic performance and emergoenvironmental impact. The system achieves daily heating savings of 3302 kWh and life cycle economic benefits of $19,310,524, with a repayment of the investment cost in 4.17 years, reflecting considerable economic benefits. This project also demonstrates exergy flow, module defects, and improvement mechanisms from the perspective of emergoenvironmental, providing a valuable reference for the promotion of green energy.

Three-objective optimization of a concentrated photovoltaic thermoelectric system via student psychology-based optimization algorithm and an external archive strategy

Thermoelectric generators (TEGs) present a promising avenue for capturing waste heat from photovoltaic (PV) panels in a hybrid concentrated photovoltaic thermoelectric (CPV-TE) setup. However, advancements in CPV-TE technology, especially in optimizing design parameters, remain crucial. A comprehensive thermodynamic analysis reveals non-monotonic, coupling influences of design factors on system performances. In light of this, we tackle a multi-criteria optimization problem using the Student Psychology-Based Optimization (SPBO) algorithm with an external archive strategy. This approach successfully identifies a range of alternative (Pareto frontier) solutions in a three-objective optimization scenario. Results indicate that setting grid density and repertoire number to 10 and 100, respectively, ensures both excellent convergence and computational efficiency. Further, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method aids in selecting an energy-efficient solution from the Pareto frontier. In an efficiency-oriented strategy, total output power, overall efficiency, and TEG power generation stand at 491.32 W, 10.85 %, and 15.91 W, respectively. Conversely, prioritizing total power output yields figures of 540.9 W, 9.61 %, and 26.35 W, respectively.

Multi-aspect assessment and optimization of a poly-generation layout composed of a geothermal power plant and a wind turbine

The current research is an effort to propose a novel poly-generation layout by hybridization of wind power and geothermal energy as two renewable heat sources. The geothermal unit comprises a steam flash cycle as the topping system and a Kalina flash cycle as the bottoming system. Hot water is produced through the utilization of the energy of the geothermal water before reinjection to the ground. An E126/7580 wind turbine is accountable for generating the favorable output electricity. Moreover, the waste heat from the generator of the wind turbine is recovered in a trilateral flash cycle. The sum of power produced by the steam flash cycle and the trilateral flash cycle is employed to generate hydrogen in an electrolysis unit. Moreover, the output electricity of the Kalina flash cycle is employed to produce cooling and freshwater in a transcritical CO2 refrigeration cycle and a reverse osmosis desalination system. The final evaluation of the system utilizing energy and exergy-economic analyses and three-objective optimization of the layout revealed an exergetic efficiency of 37.11 % in the optimum case. The configuration produces 771.7 kW, 454.3 kW, 3585 kW, 1.29 kgh-1, and 3 kgs-1 of electricity, cooling, heating, hydrogen, and freshwater, correspondingly. The economic analysis of the system indicates a total cost rate of 140.2 $h-1, a specific cost of products equal to 19.5 $GJ-1, and a payback period of 1.03 years, suggesting an excellent economic performance for such a complex system.

Multi-objective placement and sizing of energy hubs in energy networks considering generation and consumption uncertainties

This paper presents the placement and sizing of energy hubs (EHs) in electricity, gas, and heating networks. EH is a coordinator framework for various power sources, storage devices, and responsive loads. For simultaneous modeling of economic, operation, reliability, and flexibility indices, the proposed scheme is expressed as a three-objective optimization in the form of Pareto optimization based on the sum of weighted functions. The objective functions of this problem respectively minimize the planning cost of EHs (equal to the total cost of construction of hubs and their expected operating cost), the expected energy loss of the mentioned networks, and the expected energy not-supplied (EENS) of these networks in the case of an N − 1 event. The problem is constrained by power flow equations and operation and reliability constraints of these network together with the EH planning and operation model, and flexibility constraints of the EHs. Then, to achieve unique optimal solution in the shortest possible time, a linear approximation model is extracted for the proposed scheme. Moreover, scenario-based stochastic programming (SBSP) is employed to model uncertainties of load, energy cost, renewable power, and accessibility of the mentioned network equipment. Finally, the obtained numerical results indicate the capability of the proposed scheme in enhancing the economic and flexibility situation of EHs and improving the reliability and operation status of energy networks along with achieving optimal planning and operation for EHs.

Multi-objective hierarchical strategy for university dorm renovation in severe cold areas

In China, there are many old buildings whose renovation could enhance building environment quality, reduce energy consumption, lower carbon emissions, and create economic benefits. However, there is currently a lack of efficient architectural renovation optimization methods to provide decision-makers and designers with highly feasible renovation plans. This paper proposes an innovative optimization method for renovating old buildings to address this issue. The research employs a hierarchical optimization framework integrating Artificial Neural Networks (ANN), the NSGA-II algorithm, and the entropy-weighted TOPSIS method. The first layer's three-objective optimization using the trained neural network model significantly improves the algorithm's efficiency in searching for optimal solutions. The second layer's entropy-weighted TOPSIS optimization includes eight objective optimizations, such as energy, economy, and lighting, thus broadening the assessment dimensions. Taking an old multi-story university dormitory in the Shenyang area as an example, the study optimizes nine passive design variables across eight objectives. The hierarchical optimization results show that the high-scoring renovation plans can save 1.52 to 3.17 million yuan in energy costs over the building's remaining lifecycle and reduce carbon dioxide emissions by 644–962 kg per square meter. SRRC results indicate that external windows significantly impact economic efficiency and energy consumption, emphasizing the importance of selecting the right type of windows in actual engineering projects. The new method proposed in this study can offers a comprehensive and highly feasible renovation plan for old buildings.

Multi-objective optimization and 3E analysis for solar-driven dual ejector refrigeration and phase change material as thermal energy storage

Ejector refrigeration cycles have garnered attention due to their inherent advantages, including simplicity, the ability to leverage low-temperature energy sources, and reduced reliance on high electrical power requirements. Optimizing these systems becomes pivotal, given the inherent challenges of low efficiency in ejector refrigeration cycles. This study explores an ejector refrigeration cycle incorporating a secondary ejector to recover expansion energy from the condenser. Solar collectors provide the necessary heat for the cycle, utilizing phase change materials for thermal storage. Initially, the impact of five key parameters, generator temperature, condenser temperature, evaporator temperature, collector area, and storage tank volume, on the cycle's performance is thoroughly investigated. Subsequently, a three-objective optimization is executed, resulting in optimal values for the mentioned parameters: 83.4 °C for the generator temperature, 35.8 °C for the condenser temperature, 6.8 °C for the evaporator temperature, 82 m2 for the collector area, and 4 m3 for the storage tank volume. The analysis of energy, exergy and exergeoeconomics for the optimal point showed that the exergy efficiency for the ejector and solar cycle is 6.78 % and 10.74 %, respectively. Also, the COP of the refrigeration cycle was obtained as 0.1858. The results showed that the solar fraction is equal to 65.4 %, and the highest amount of exergy destruction is related to solar collectors, which is equal to 8.136 kW.

Designing a multi-objective energy management system in multiple interconnected water and power microgrids based on the MOPSO algorithm

In this paper, a method of the energy management system (EMS) in multiple microgrids considering the constraints of power flow based on the three-objective optimization model is presented. The studied model specifications, the variable speed pumps in the water network as well and the storage tanks are optimally planned as flexible resources to reduce operating costs and pollution. The proposed method is implemented hierarchically through two primary and secondary control layers. At the primary control level, each microgrid performs local planning for its subscribers and energy generation resources, and their excess or unsupplied power is determined. Then, by sending this information to the central energy management system (CEMS) at the secondary level, it determines the amount of energy exchange, taking into account the limitations of power flow. Energy storage systems (ESS) are also considered to maintain the balance between power generation by renewable energy sources and consumption load. Also, the demand response (DR) program has been used to smooth the load curve and reduce operating costs. Finally, in this article, the multi-objective particle swarm optimization (MOPSO) is used to solve the proposed three-objective problem with three cost functions generation, pollution, and pump operation. Additionally, sensitivity analysis was conducted with uncertainties of 25 % and 50 % in network generation units, exploring their impact on objective functions. The proposed model has been tested on the microgrid of a 33-bus test distribution and 15-node test water system and has been investigated for different cases. The simulation results prove the effectiveness of the integration of water and power network planning in reducing the operating cost and emission of pollution in such a way that the proposed control scheme properly controls the performance of microgrids and the network in interactions with each other and has a high level of robustness, stable behavior under different conditions and high quality of the power supply. In such a way that improvements of 41.1 %, 52.2 %, and 20.4 % in the defined objective functions and the evaluation using DM, SM, and MID indices further confirms the algorithm's enhanced performance in optimizing the specified objective functions by 51 %, 11 %, and 5.22 %, respectively.

Multi-objective optimization for low hydrogen consumption and long useful life in fuel cell emergency power supply systems

The power system using hydrogen fuel cells (FCs) as the primary energy source holds significant potential for applications in emergency fields such as post-disaster relief and outdoor power supply. The operational environment of the FC emergency power supply system (FCEPSS) is challenging, with long continuous operation time, difficulties in hydrogen supply, high maintenance costs, and, accordingly, requirements for hydrogen consumption and useful life of FCs are heightened. Hence, this paper proposes a multi-objective energy management strategy (EMS) for FCEPSS. First, the structure of FCEPSS is designed based on the demand analysis in emergency scenarios. Then, the hydrogen consumption and life degradation models of FCEPSS are established. Aiming at the multi-objective optimization problem of minimizing hydrogen consumption and life degradation value in the FCEPSS, an improved multi-objective optimization algorithm is proposed. This paper converts complex constraints into the penalty term and incorporates it as one of the objectives, constructing a three-objective optimization model. To enhance the solution of multi-objective optimization problems, an improved strategy based on relaxation-tightening and three-objective sorting is proposed. Combining the fast non-dominated sorting genetic algorithm-II and the above improved strategy, an improved multi-objective optimization algorithm (IMOOA) is constructed. In the simulation, the advantages of the proposed IMOOA algorithm are verified. In the experimental part, compared with the logical threshold strategy, the proposed strategy saves 0.60% of hydrogen consumption and reduces 5.65% of life degradation within 24 h, respectively. Finally, the superiority of the multi-objective EMS utilizing the IMOOA is demonstrated.

Data driven multi-objective design for low-carbon self-compacting concrete considering durability

Self-Compacting Concrete (SCC) offers remarkable benefits in modern engineering. However, traditional SCC design faces challenges, necessitating a reduction in carbon emissions for enhanced sustainability and a shift towards multi-performance collaborative design. This study proposed an intelligent and interpretable approach for multi-objective low-carbon SCC design. By combining machine learning and optimization algorithm, key factors including sustainability, rheology, workability, strength, durability, and cost were simultaneously addressed. Partial Dependence Plots was employed for model interpretation and feature impacts reveal. The proposed three-objective optimization exhibited superior efficiency, achieving comprehensive optimization efficiencies of 42.3%. In the context of meeting other performance requirements, C40 and C50 SCC optimized using this method exhibited a significant reduction of 18.9% and 10.1% in embodied carbon. This study aims to establish a versatile intelligent framework, rather than a specific model, capable of adapting to future iterations with evolving high-quality data, to achieve multi-objective SCC collaborative design. This advancement contributes to intelligent and low-carbon practices in concrete science and the construction industry.

Chemical looping combustion-driven cooling and power cogeneration system with LNG cold energy utilization: Exergoeconomic analysis and three-objective optimization

This study conducts thermodynamic and exergoeconomic analyses for a novel chemical looping combustion-driven cooling and power cogeneration system consisting of a supercritical carbon dioxide cycle, an air cycle, an organic Rankine cycle, and a liquid natural gas regasification process. Comprehensive parametric analyses are performed to investigate the impact of design variables on the proposed system's performance metrics, including electrical efficiency (ηel), exergy efficiency (ηex), and total product unit cost (cp,tot). A three-objective optimization is implemented to obtain the optimal system performance. The results underscore that the two reactors exhibit the highest exergy destruction of 17.29 MW, followed by the condenser's exergy destruction of 6.50 MW. The two reactors, expander, gas turbine, and condenser are the most important components within the system, and evaporator 1 and condenser need to be given more attention from exergoeconomic aspects. An optimum split ratio exists to maximize system performance by optimizing the conditions of the recycled stream into the fuel reactor. Optimization results reveal that ηel, ηex, and cp,tot cannot be simultaneously optimal and are 52.68%, 40.72%, and 27.43 $/GJ, respectively, after weighing. Moreover, the weighed performance of the proposed system outperforms those of similar systems despite employing components with lower design efficiencies.

Characteristics analysis and structure optimization of a hybrid micro-jet impingement/micro-channel heat sink

To solve the heat dissipation problem of high-power electronics, a novel hybrid micro-jet impingement/micro-channel heat sink is proposed in this work. Different from the continuous and straight rectangular micro-channel, the inclined and interrupted channel walls (fins) are adopted to enhance the fluid disturbance inside the heat sink. To obtain the thermal–hydraulic performance of the hybrid heat sink, an overall 3D modeling and numerical simulations are conducted instead of local cell numerical simulation. In addition, to further improve the heat transfer and flow resistance performance, the effects of variation of structural parameters on heat transfer and flow resistance properties are investigated, including the fin inclination angle, fin length and fin height. The results of structural parametric analysis reveal that there are interactions between different structural parameters, but with different levels of significance for different evaluation indicators. Then, to obtain the optimal combinations of structural parameters for different applications, the multi-objective genetic algorithm coupled with artificial neural network (ANN) is utilized to search for the optimal value of each structural parameter. The heat transfer thermal resistance, pressure drop and mean absolute temperature deviation of the heated surface are utilized as the optimization objectives to evaluate the cooling and resistance characteristics. The optimization results obtained by three-objective optimization and two-objective optimization are analyzed and compared. Finally, seven optimal compromise solutions are selected from the Pareto front by using TOPSIS algorithm with different weight factor matrices. For the optimal compromise solutions, the maximum relative errors between the predicted values of the ANN and numerical simulation values are only 3%, which further demonstrates the accuracy of the artificial neural network model and the effectiveness of using multi-objective optimization to guide heat sink design.

Exergy and Environmental Analysis for Optimal Condition Finding of a New Combined Cycle

In this paper, various thermal energy systems are studied to recover waste heat from gas turbines with different configurations. The exergy analysis and environmental examination are applied to achieve better insight into the suggested systems. Also, multi-objective optimization is employed to find the optimal condition of the introduced plants. In this work, various systems such as gas turbine (GT), organic Rankine cycle (ORC), and Kalina cycle (KC) with Proton Exchange Membrane (PEM) electrolyzer are combined to achieve a new system design. In this study, Engineering Equation Solver (V11.755) and Matlab (R2023a) software are used to simulate and optimize the proposed system. The comparison of systems shows that the combustion chamber with 3622 kW has the most considerable exergy destruction in the IGT/ORC-KC plant. The comparative investigation shows that IGT/ORC-KC has the highest output at 5659 kW, while the smallest exergy destruction is associated with the IGT system with 1779 kW. The multi-objective optimization considering three objective functions, namely, exergy efficiency, product cost, and environmental effects of exergy destruction, is conducted. Three-objective optimization on the IGT/ORC-KC unit shows that in the optimum point selected by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach, the exergy efficiency, cost of product, and environmental effect of exergy destruction rate are 29.5%, 0.31 USD/kWh, and 13.22 mPt/s.

Techno-economic Assessment and Optimization of a Solar-Driven Power and Hydrogen Co-generation Plant Retrofitted with Enhanced Energy Storage

Pioneering solar-driven power and co-generation facilities stands as a crucial stride toward decarbonizing energy systems. Moreover, the production of green hydrogen serves as the cornerstone of decarbonized energy systems. This study evaluates a solar-driven co-generation plant for power and hydrogen production employing the vanadium-chlorine cycle. The plant is integrated with a thermal storage system of solid materials as a packed bed to increase the capacity factor. Three cases are studied based on energy storage materials. The first case uses alumina, while the second and third cases utilize steel slag with varying compounds. Following an extensive sensitivity analysis, an artificial neural network is formulated to subject each case to three-objective optimization using a genetic algorithm. Moreover, Seville in Spain is selected as the designated case study, and real-world solar availability data is employed to enhance the authenticity and realism of the results. Results show using steel slag as an energy storage material instead of alumina reduces the levelized cost of energy and the cost of thermal energy storage units by 14 and 69%, respectively. From both economic and environmental perspectives, the second case emerges as the most favorable option. Through the implementation of a 36.3-hectare solar farm in Seville, the second case generates 38.5 GWh electricity and 2.1 kton hydrogen with a cost of 189 USD/MWh and 7.2 USD/kg, respectively, factoring in expenses for hydrogen compression and storage facilities. This case has the potential to reduce Spain's CO2 emissions by 51 kton per year.

A modified Diesel cycle via isothermal heat addition, its endoreversible modelling and multi-objective optimization

A new model of thermal cycle, modified Diesel cycle, is constructed via isothermal heat addition. Endorebersible modelling is performed considering heat transfer loss. Power (P), efficiency (η), power density (Pd) and ecological function (E) are deduced and evaluated. Characteristic relationships are deduced by using the finite-time thermodynamics theory. Cycle performances under the conditions of the maximum E, the maximum Pd, the maximum η, and the maximum P are compared. Under the same conditions, four performance functions of modified Diesel cycle and traditional Diesel cycle are compared. Based on the algorithm of NSGA-II, taking four performance indictors as the optimization objectives and the compression ratio as the optimization variable, multi-objective optimizations of the modified and traditional Diesel cycles under different objective combinations are carried out. Three decision-making method are used to select a group of optimal solutions. Compared with traditional Diesel cycle, performances P, η, Pd and E of modified Diesel cycle are improved at least by 8.69 %, 0.97 %, 8.23 %, and 8.64 %, respectively. Modified Diesel cycle with Pd and E for two-objective optimization is optimal, and the minimum deviation index is 0.1336, while the traditional Diesel cycle with P, η and Pd for three-objective optimization is optimal, and the minimum deviation index is 0.1339.

A multi-objective optimization approach for a fault geothermal system based on response surface method

A fault geothermal system is in operation with a multi-objective optimization for maximum heat extraction, minimum reservoir impedance and minimum construction cost (these are three objectives in this study). Its heat extraction efficiency heavily depends on the interaction of fluid flows in wells and faults. So far, this multi-objective optimization problem is usually solved through single-objective analysis. This study proposes a multi-objective optimization approach for a fault geothermal system based on the geological conditions in Western Anatolia, Turkey and response surface method. First, a numerical simulation model for heat extraction is established by using high permeability faults as main flow paths. Second, a surrogate model is proposed for the prompt response of the three-objective optimization to largely reduce the huge computation load in direct optimization. Third, a reasonable group of simulation runs are designed with an I-optimal method and the surrogate model is determined through the least square fitting on the simulation run results. Finally, the Pareto front of the three objectives is obtained through a multi-objective evolutionary algorithm and an optimal operation scheme is obtained. These results show that the experiment design and the response surface method can practically replace the complex numerical simulations in a surrogate model, thus largely reducing the computation load in the multi-objective optimization for a fault geothermal system.